Sample description

Institutions per country

universities_per_country <- works %>% 
  distinct(country_code, institution_id) %>% 
  count(country_code, name = "n_universities") %>% 
  arrange(desc(n_universities)) %>% 
  collect() %>% 
  add_country()

universities_per_country %>% 
  select(country, n_universities) %>% 
  knitr::kable()
country n_universities
United States 6808
India 2061
China 1985
United Kingdom 1610
Japan 1444
Germany 1033
France 1024
Brazil 807
Canada 746
Russian Federation 665
Spain 642
Italy 582
Australia 538
Korea, Rep. 478
NA 433
Indonesia 407
Poland 333
Netherlands 329
Switzerland 293
Taiwan, China 264
Turkiye 240
Pakistan 240
Mexico 236
Iran, Islamic Rep. 234
Norway 219
Colombia 205
Ukraine 199
Czech Republic 195
Sweden 186
Portugal 181
Austria 177
Nigeria 171
South Africa 168
Belgium 158
Thailand 156
Argentina 151
Greece 145
Finland 144
Denmark 142
Ireland 138
Bangladesh 132
Philippines 122
Malaysia 121
Israel 120
Hungary 112
New Zealand 108
Vietnam 103
Chile 97
Singapore 97
Egypt, Arab Rep. 96
Saudi Arabia 91
Slovak Republic 89
Kenya 86
Romania 80
Bulgaria 80
Peru 78
Slovenia 76
Ecuador 65
Uganda 64
United Arab Emirates 60
Serbia 60
Croatia 59
Ghana 54
Ethiopia 54
Iraq 52
Kazakhstan 51
Tanzania 48
Algeria 47
Nepal 46
Belarus 40
Venezuela, RB 39
Sri Lanka 38
Cuba 38
Latvia 38
Lithuania 36
Tunisia 33
Estonia 33
Jordan 32
Zimbabwe 28
Morocco 28
Sudan 27
Uruguay 27
Uzbekistan 27
Cameroon 27
Lebanon 26
Cyprus 26
Costa Rica 25
Luxembourg 23
Armenia 23
Georgia 22
Oman 21
Cambodia 20
Albania 20
Bolivia 20
Azerbaijan 19
Iceland 19
Kuwait 19
Qatar 19
Myanmar 19
Syrian Arab Republic 19
Bosnia and Herzegovina 19
Congo, Dem. Rep. 18
Paraguay 17
West Bank and Gaza 17
Zambia 16
Malawi 15
Dominican Republic 15
Panama 15
Moldova 14
Mozambique 14
Mongolia 14
Senegal 14
Kyrgyz Republic 14
Yemen, Rep. 14
North Macedonia 13
Bahrain 13
Guatemala 13
Burkina Faso 13
Rwanda 12
Hong Kong SAR, China 11
Afghanistan 10
Cote d'Ivoire 10
Botswana 10
Libya 10
Nicaragua 9
Tajikistan 9
El Salvador 9
Trinidad and Tobago 8
Somalia 8
Angola 7
Namibia 7
Madagascar 7
Papua New Guinea 7
Mali 6
Niger 6
Mauritius 6
Malta 6
Gambia, The 5
Benin 5
Lao PDR 5
Montenegro 5
Fiji 5
Honduras 5
Kosovo 5
Jamaica 5
Brunei Darussalam 4
Gabon 4
Korea, Dem. People's Rep. 4
Curacao 4
Togo 4
South Sudan 4
Guinea-Bissau 3
Bahamas, The 3
Liechtenstein 3
Belize 3
Puerto Rico 3
Sierra Leone 3
Bermuda 3
French Polynesia 3
Mauritania 3
Greenland 3
Turkmenistan 3
Liberia 3
Monaco 3
Maldives 3
St. Kitts and Nevis 3
NA 2
Antigua and Barbuda 2
Guyana 2
Haiti 2
Seychelles 2
Suriname 2
Congo, Rep. 2
Sint Maarten (Dutch part) 2
New Caledonia 2
St. Lucia 2
NA 2
Macao SAR, China 2
Eswatini 2
Bhutan 2
Chad 2
Burundi 2
NA 1
Aruba 1
Guinea 1
Grenada 1
Palau 1
NA 1
Timor-Leste 1
British Virgin Islands 1
Faroe Islands 1
St. Vincent and the Grenadines 1
Isle of Man 1
Sao Tome and Principe 1
Andorra 1
NA 1
Barbados 1
NA 1
NA 1
Cabo Verde 1
Cayman Islands 1
Eritrea 1
NA 1
Marshall Islands 1
Micronesia, Fed. Sts. 1
Gibraltar 1
Lesotho 1
NA 1
Central African Republic 1 
# papers per country
papers_per_country <- works %>% 
  distinct(country_code, id, work_frac, author_position, institution_id) %>% 
  group_by(country_code) %>% 
  summarise(sum_fractional_works = sum(work_frac) %>% round(digits = 1)) %>% 
  arrange(desc(sum_fractional_works)) %>% 
  collect() %>% 
  add_country()
papers_per_country %>% 
  select(country, country_code, sum_fractional_works) %>% 
  knitr::kable()
country country_code sum_fractional_works
United States US 747996.2
China CN 595755.7
Brazil BR 400590.8
United Kingdom GB 194942.3
Germany DE 170473.8
Japan JP 148536.0
Spain ES 120279.7
India IN 118094.2
Canada CA 115456.2
Italy IT 104839.5
Korea, Rep. KR 103472.3
Australia AU 100722.0
France FR 89289.2
Netherlands NL 61987.0
Taiwan, China TW 58803.5
Poland PL 58718.3
Indonesia ID 50570.5
Iran, Islamic Rep. IR 48697.3
Sweden SE 48538.7
Switzerland CH 42420.7
Mexico MX 40329.8
Russian Federation RU 37819.3
South Africa ZA 37177.8
Malaysia MY 36006.3
Turkiye TR 32717.7
Belgium BE 32310.3
Colombia CO 29179.2
Denmark DK 29004.8
Portugal PT 27315.5
Norway NO 26270.0
Austria AT 21768.8
Argentina AR 21129.7
Pakistan PK 20774.2
Czech Republic CZ 20532.3
Saudi Arabia SA 20301.3
Israel IL 20143.3
Finland FI 19189.3
Chile CL 18898.0
Singapore SG 17013.7
Egypt, Arab Rep. EG 16972.7
Thailand TH 15408.0
Greece GR 13735.5
New Zealand NZ 13046.3
Nigeria NG 13013.3
Ireland IE 12550.7
Ukraine UA 10561.5
Hungary HU 10517.8
Serbia RS 9506.3
NA NA 9483.0
Ethiopia ET 9428.2
Romania RO 7396.5
Croatia HR 7322.8
Slovak Republic SK 7011.0
Bangladesh BD 6685.3
Slovenia SI 6396.3
Peru PE 6205.5
Morocco MA 5027.8
Ghana GH 4885.5
Bahrain BH 4004.8
Lithuania LT 3961.0
Ecuador EC 3926.5
Bulgaria BG 3555.0
Kenya KE 3518.5
Vietnam VN 3455.3
Nepal NP 3416.3
Costa Rica CR 3380.8
United Arab Emirates AE 3175.5
Sri Lanka LK 2985.8
Uganda UG 2887.3
Estonia EE 2875.0
Iraq IQ 2854.2
Lebanon LB 2848.5
Uruguay UY 2814.8
Qatar QA 2813.7
Jordan JO 2812.8
Tunisia TN 2727.0
Philippines PH 2368.5
Cameroon CM 2285.5
Tanzania TZ 2216.7
Venezuela, RB VE 1633.7
Cyprus CY 1630.2
Oman OM 1593.0
Kuwait KW 1584.2
Benin BJ 1381.8
Armenia AM 1364.0
Hong Kong SAR, China HK 1261.2
Mozambique MZ 1257.3
Bosnia and Herzegovina BA 1202.7
Latvia LV 1190.7
Luxembourg LU 1142.5
Iceland IS 1110.3
Cuba CU 1097.3
Algeria DZ 1059.3
Zimbabwe ZW 1017.7
Kazakhstan KZ 999.8
Puerto Rico PR 958.5
Sudan SD 897.5
Antigua and Barbuda AG 881.7
Jamaica JM 881.0
West Bank and Gaza PS 879.7
Zambia ZM 706.8
Belarus BY 668.5
Paraguay PY 634.5
Cambodia KH 623.5
Lao PDR LA 621.5
El Salvador SV 566.0
Malawi MW 553.0
Syrian Arab Republic SY 551.8
Panama PA 550.8
Malta MT 506.0
Senegal SN 480.5
Uzbekistan UZ 467.0
Botswana BW 450.0
Kosovo XK 422.5
Azerbaijan AZ 383.3
North Macedonia MK 375.8
Burkina Faso BF 370.0
Bolivia BO 368.3
Montenegro ME 365.3
Yemen, Rep. YE 335.7
Georgia GE 308.3
Guatemala GT 305.7
NA RE 304.0
Angola AO 299.5
Albania AL 299.3
Mali ML 291.3
Nicaragua NI 288.3
Mongolia MN 287.7
St. Kitts and Nevis KN 276.3
Rwanda RW 266.0
Brunei Darussalam BN 250.5
Congo, Dem. Rep. CD 242.5
Cote d'Ivoire CI 235.8
Sao Tome and Principe ST 232.0
Libya LY 226.0
Moldova MD 207.0
Grenada GD 181.2
Namibia NA 179.3
Gabon GA 163.8
Myanmar MM 156.5
Mauritius MU 155.2
Fiji FJ 153.5
NA GP 153.3
Barbados BB 152.0
Togo TG 138.5
Papua New Guinea PG 131.3
Dominican Republic DO 127.3
Honduras HN 123.8
Bahamas, The BS 109.5
Niger NE 102.8
French Polynesia PF 90.5
NA SJ 81.3
Tajikistan TJ 77.0
Kyrgyz Republic KG 74.3
Congo, Rep. CG 61.8
Guinea-Bissau GW 57.8
Guinea GN 56.0
Afghanistan AF 55.8
Korea, Dem. People's Rep. KP 54.5
Madagascar MG 53.0
Lesotho LS 51.0
Trinidad and Tobago TT 46.0
Sierra Leone SL 45.0
British Virgin Islands VG 44.0
Bermuda BM 44.0
Suriname SR 40.0
Eswatini SZ 40.0
Gambia, The GM 39.0
Bhutan BT 35.0
Faroe Islands FO 31.8
New Caledonia NC 30.5
Cabo Verde CV 29.5
Curacao CW 29.0
NA GF 25.5
Greenland GL 25.5
Guyana GY 21.5
Burundi BI 19.5
Mauritania MR 17.0
Somalia SO 14.0
Liechtenstein LI 13.5
Liberia LR 12.5
St. Vincent and the Grenadines VC 11.0
South Sudan SS 10.0
Sint Maarten (Dutch part) SX 8.0
St. Lucia LC 6.5
Chad TD 6.5
Monaco MC 6.3
NA JE 6.0
Gibraltar GI 5.5
Cayman Islands KY 5.0
Maldives MV 4.5
Marshall Islands MH 4.5
NA MQ 4.0
Belize BZ 4.0
Turkmenistan TM 4.0
Haiti HT 4.0
Macao SAR, China MO 3.0
Micronesia, Fed. Sts. FM 3.0
Central African Republic CF 3.0
Palau PW 3.0
Seychelles SC 2.0
Eritrea ER 2.0
Andorra AD 2.0
Timor-Leste TL 2.0
Aruba AW 1.5
Isle of Man IM 1.0
NA VA 1.0
NA MS 1.0
NA AX 0.5 
# average apc
average_apc <- works %>%
  # first get rid of duplicates from concepts
  distinct(country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar) %>% 
  group_by(country_code) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country_code, sum_frac) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect() %>% 
  add_country()
## `summarise()` has grouped output by 'country_code'. You can override using the
## `.groups` argument.
# average APC over time
average_apc_time <- works %>%
  # first get rid of duplicates from concepts
  distinct(country_code, id, work_frac, author_position, institution_id,
           APC_in_dollar, publication_year) %>% 
  group_by(country_code, publication_year) %>%
  # compute the average APC using fractional authorships as weights
  mutate(sum_frac = sum(work_frac)) %>%
  group_by(country_code, sum_frac, publication_year) %>%
  summarise(mean_apc = sum(work_frac * APC_in_dollar) / sum_frac) %>% 
  collect() 
## `summarise()` has grouped output by 'country_code', 'sum_frac'. You can override
## using the `.groups` argument.
average_apc_time %>% 
  left_join(wdi, by = c("country_code" = "iso2c")) %>% 
  ggplot(aes(publication_year, mean_apc)) +
  geom_line(aes(group = country), alpha = .3) +
  geom_smooth(se = FALSE, colour = "#007FA8") +
  facet_wrap(vars(region)) +
  scale_x_continuous(breaks = scales::pretty_breaks(6)) +
  coord_cartesian(ylim = c(0, 3000)) +
  labs(x = NULL, y = "Mean APC")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

All three joined

all_three_descriptives <- universities_per_country %>% 
  left_join(papers_per_country, by = c("country", "country_code")) %>% 
  left_join(average_apc, by = c("country", "country_code")) %>% 
  # remove missing values
  # these arise since the wdi data on country codes does not have a mapping for
  # all codes that are present in the data. in most cases, these are small 
  # countries, and in the case of a high university count (433) simply all
  # institutions that were not assigned a country code at all.
  drop_na()

all_three_descriptives %>% 
  arrange(desc(n_universities), desc(sum_fractional_works)) %>% 
  mutate(mean_apc = round(mean_apc, digits = 1),
         sum_fractional_works = scales::comma(sum_fractional_works)) %>% 
  select(Country = country, `n universities` = n_universities,
         `n fractional publications` = sum_fractional_works, 
         `Mean APC` = mean_apc) %>% 
  knitr::kable()
Country n universities n fractional publications Mean APC
United States 6808 747,996.20 2020.0
India 2061 118,094.20 861.8
China 1985 595,755.70 1969.9
United Kingdom 1610 194,942.30 2011.3
Japan 1444 148,536.00 1881.5
Germany 1033 170,473.80 2029.1
France 1024 89,289.20 1926.6
Brazil 807 400,590.80 381.1
Canada 746 115,456.20 1900.4
Russian Federation 665 37,819.30 700.5
Spain 642 120,279.70 1285.0
Italy 582 104,839.50 1900.0
Australia 538 100,722.00 1991.2
Korea, Rep. 478 103,472.30 1816.7
Indonesia 407 50,570.50 203.2
Poland 333 58,718.30 1086.9
Netherlands 329 61,987.00 2063.3
Switzerland 293 42,420.70 2091.2
Taiwan, China 264 58,803.50 1892.1
Turkiye 240 32,717.70 880.4
Pakistan 240 20,774.20 1207.6
Mexico 236 40,329.80 931.0
Iran, Islamic Rep. 234 48,697.30 779.7
Norway 219 26,270.00 1859.5
Colombia 205 29,179.20 365.5
Ukraine 199 10,561.50 403.7
Czech Republic 195 20,532.30 1387.1
Sweden 186 48,538.70 1990.2
Portugal 181 27,315.50 1138.9
Austria 177 21,768.80 1943.7
Nigeria 171 13,013.30 934.5
South Africa 168 37,177.80 1211.7
Belgium 158 32,310.30 1916.2
Thailand 156 15,408.00 1564.1
Argentina 151 21,129.70 789.1
Greece 145 13,735.50 1603.3
Finland 144 19,189.30 1862.1
Denmark 142 29,004.80 2002.7
Ireland 138 12,550.70 1928.1
Bangladesh 132 6,685.30 979.0
Philippines 122 2,368.50 1110.9
Malaysia 121 36,006.30 1201.9
Israel 120 20,143.30 2103.5
Hungary 112 10,517.80 1642.5
New Zealand 108 13,046.30 1756.2
Vietnam 103 3,455.30 1452.4
Chile 97 18,898.00 925.9
Singapore 97 17,013.70 2075.7
Egypt, Arab Rep. 96 16,972.70 992.4
Saudi Arabia 91 20,301.30 1434.5
Slovak Republic 89 7,011.00 839.4
Kenya 86 3,518.50 1675.9
Romania 80 7,396.50 1154.0
Bulgaria 80 3,555.00 707.3
Peru 78 6,205.50 296.0
Slovenia 76 6,396.30 1233.3
Ecuador 65 3,926.50 404.3
Uganda 64 2,887.30 1898.8
Serbia 60 9,506.30 697.1
United Arab Emirates 60 3,175.50 1585.9
Croatia 59 7,322.80 675.8
Ethiopia 54 9,428.20 1686.4
Ghana 54 4,885.50 1569.8
Iraq 52 2,854.20 757.3
Kazakhstan 51 999.80 1065.0
Tanzania 48 2,216.70 1806.2
Algeria 47 1,059.30 586.5
Nepal 46 3,416.30 859.4
Belarus 40 668.50 823.3
Venezuela, RB 39 1,633.70 428.8
Sri Lanka 38 2,985.80 1337.2
Latvia 38 1,190.70 705.7
Cuba 38 1,097.30 734.6
Lithuania 36 3,961.00 1223.8
Estonia 33 2,875.00 1342.4
Tunisia 33 2,727.00 1236.8
Jordan 32 2,812.80 1331.7
Morocco 28 5,027.80 1730.6
Zimbabwe 28 1,017.70 1243.5
Uruguay 27 2,814.80 654.1
Cameroon 27 2,285.50 1630.5
Sudan 27 897.50 1445.8
Uzbekistan 27 467.00 537.9
Lebanon 26 2,848.50 1615.8
Cyprus 26 1,630.20 1506.0
Costa Rica 25 3,380.80 230.2
Armenia 23 1,364.00 1231.6
Luxembourg 23 1,142.50 1998.6
Georgia 22 308.30 1177.3
Oman 21 1,593.00 645.0
Cambodia 20 623.50 1248.0
Bolivia 20 368.30 359.0
Albania 20 299.30 721.8
Qatar 19 2,813.70 1525.4
Kuwait 19 1,584.20 1561.7
Bosnia and Herzegovina 19 1,202.70 432.0
Iceland 19 1,110.30 1617.8
Syrian Arab Republic 19 551.80 987.2
Azerbaijan 19 383.30 637.8
Myanmar 19 156.50 816.9
Congo, Dem. Rep. 18 242.50 1597.4
West Bank and Gaza 17 879.70 1524.6
Paraguay 17 634.50 124.7
Zambia 16 706.80 1683.8
Malawi 15 553.00 1939.3
Panama 15 550.80 1551.3
Dominican Republic 15 127.30 786.4
Mozambique 14 1,257.30 432.3
Senegal 14 480.50 1294.7
Yemen, Rep. 14 335.70 1132.6
Mongolia 14 287.70 719.8
Moldova 14 207.00 1016.1
Kyrgyz Republic 14 74.30 1226.4
Bahrain 13 4,004.80 1727.7
North Macedonia 13 375.80 604.3
Burkina Faso 13 370.00 1867.9
Guatemala 13 305.70 700.8
Rwanda 12 266.00 1712.2
Hong Kong SAR, China 11 1,261.20 1957.9
Botswana 10 450.00 1019.1
Cote d'Ivoire 10 235.80 1294.0
Libya 10 226.00 887.8
Afghanistan 10 55.80 967.3
El Salvador 9 566.00 52.6
Nicaragua 9 288.30 210.6
Tajikistan 9 77.00 405.7
Trinidad and Tobago 8 46.00 925.9
Somalia 8 14.00 1131.0
Angola 7 299.50 191.4
Namibia 7 179.30 1173.9
Papua New Guinea 7 131.30 1562.2
Madagascar 7 53.00 1411.7
Malta 6 506.00 1247.0
Mali 6 291.30 2015.2
Mauritius 6 155.20 1010.4
Niger 6 102.80 1669.3
Benin 5 1,381.80 1816.5
Jamaica 5 881.00 1466.1
Lao PDR 5 621.50 1684.1
Kosovo 5 422.50 777.0
Montenegro 5 365.30 548.9
Fiji 5 153.50 1293.4
Honduras 5 123.80 931.0
Gambia, The 5 39.00 1821.5
Brunei Darussalam 4 250.50 1016.9
Gabon 4 163.80 2012.9
Togo 4 138.50 1203.7
Korea, Dem. People's Rep. 4 54.50 677.1
Curacao 4 29.00 1487.7
South Sudan 4 10.00 1751.0
Puerto Rico 3 958.50 1902.2
St. Kitts and Nevis 3 276.30 2036.7
Bahamas, The 3 109.50 177.7
French Polynesia 3 90.50 1612.2
Guinea-Bissau 3 57.80 2056.2
Sierra Leone 3 45.00 1817.6
Bermuda 3 44.00 1818.2
Greenland 3 25.50 987.3
Mauritania 3 17.00 2185.8
Liechtenstein 3 13.50 1165.2
Liberia 3 12.50 2094.9
Monaco 3 6.30 1318.7
Maldives 3 4.50 967.8
Belize 3 4.00 613.8
Turkmenistan 3 4.00 589.4
Antigua and Barbuda 2 881.70 800.9
Congo, Rep. 2 61.80 1625.9
Suriname 2 40.00 1865.9
Eswatini 2 40.00 1202.7
Bhutan 2 35.00 351.1
New Caledonia 2 30.50 1660.5
Guyana 2 21.50 881.7
Burundi 2 19.50 1057.2
Sint Maarten (Dutch part) 2 8.00 777.5
St. Lucia 2 6.50 1480.5
Chad 2 6.50 1613.7
Haiti 2 4.00 1300.2
Macao SAR, China 2 3.00 0.0
Seychelles 2 2.00 1607.5
Sao Tome and Principe 1 232.00 330.4
Grenada 1 181.20 1335.3
Barbados 1 152.00 1200.3
Guinea 1 56.00 1695.7
Lesotho 1 51.00 663.4
British Virgin Islands 1 44.00 1203.6
Faroe Islands 1 31.80 1406.3
Cabo Verde 1 29.50 435.9
St. Vincent and the Grenadines 1 11.00 922.7
Gibraltar 1 5.50 1199.1
Cayman Islands 1 5.00 416.6
Marshall Islands 1 4.50 532.8
Palau 1 3.00 1849.5
Micronesia, Fed. Sts. 1 3.00 942.7
Central African Republic 1 3.00 1391.7
Timor-Leste 1 2.00 1245.0
Andorra 1 2.00 1350.4
Eritrea 1 2.00 387.5
Aruba 1 1.50 1616.0
Isle of Man 1 1.00 2235.4 
# restrict data for plotting so we only plot countries with at least 5 universities
all_three_descriptives <- all_three_descriptives %>% 
  filter(n_universities >= 5)
gdp <- WDI::WDI(start = 2019, end = 2019)

# plot n papers against average apc
p <- all_three_descriptives %>% 
  left_join(wdi, by = c("country_code" = "iso2c", "country" = "country")) %>% 
  ggplot(aes(sum_fractional_works, mean_apc, colour = region, label = country)) +
  geom_point() +
  scale_x_log10(labels = scales::comma) +
  scale_y_continuous(labels = scales::comma) +
  theme(legend.position = "top") +
  labs(y = NULL, colour = NULL, x = "Sum of fractional publications")
p

plotly::ggplotly(p)
pdata <- all_three_descriptives %>% 
  left_join(gdp, by = c("country_code" = "iso2c", "country" = "country")) %>% 
  left_join(wdi, by = c("country_code" = "iso2c", "country" = "country"))

labels <- pdata %>% 
  mutate(label = case_when(
    country %in% c("China", "India", "United States","Uganda",
                      "Brazil", "Switzerland", "Israel", "Spain",
                     "Saudi Arabia") ~ country,
    TRUE ~ ""))

p <- pdata %>% 
  ggplot(aes(NY.GDP.PCAP.KD, mean_apc, colour = region, label = country)) +
  geom_point(aes(alpha = sum_fractional_works)) +
  ggrepel::geom_text_repel(data = labels, aes(label = label),
                           show.legend = FALSE, max.overlaps = Inf,
                           box.padding = 1, min.segment.length = 0,
                           nudge_y = -10) +
  scale_x_continuous(labels = scales::dollar) +
  scale_y_continuous(labels = scales::dollar) +
  scale_alpha_continuous(trans = "log10", range = c(.1, 1),
                         labels = scales::comma) +
  scale_colour_discrete_qualitative(palette = "Dark 3") +
  theme(legend.position = "top", legend.box = "vertical") +
  labs(y = "Mean APC", colour = NULL, x = "GDP per capita", 
       alpha = "Number of fractional publications")
p

plotly::ggplotly(p)

Across topics within continents

papers_per_country_per_field <- works %>% 
  mutate(total_weight = work_frac * concept_frac) %>% 
  distinct(country, country_code, id, author_position, institution_id, field,
           total_weight) %>% 
  group_by(country, country_code, field) %>% 
  summarise(sum_fractional_works = sum(total_weight) %>% round(digits = 1)) %>% 
  arrange(desc(sum_fractional_works)) %>% 
  collect()
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'country', 'country_code'. You can override using the `.groups` argument.
plot_data <- papers_per_country_per_field %>% 
  left_join(wdi, by = c("country_code" = "iso2c")) %>% 
  group_by(region, field) %>% 
  summarise(sum_fractional_works = sum(sum_fractional_works)) %>% 
  mutate(prop = sum_fractional_works / sum(sum_fractional_works),
         label = case_when(
           prop > .15 ~ scales::percent(prop, accuracy = .1),
           TRUE ~ "")
  )
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
plot_data %>% 
  drop_na() %>% 
  ggplot(aes(prop, fct_reorder(field, prop))) +
  geom_segment(aes(xend = 0, yend = field), colour = "grey70") +
  geom_point() +
  geom_text(aes(label = label), nudge_x = .015, hjust = "left") +
  facet_wrap(vars(region)) +
  scale_x_continuous(expand = expansion(mult = c(0.05, .25)),
                     labels = function(x) scales::percent(x, accuracy = 1)) +
  labs(x = "% of fractional publications", y = NULL) +
  theme(panel.grid = element_blank(),
        panel.grid.major.x = element_line(colour = "grey92"),
        axis.text.y = element_text(margin = margin(r = -2)))

spark_disconnect(sc)